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Articles 31 - 60 of 249
Full-Text Articles in Software Engineering
Learning Probabilistic Models For Model Checking: An Evolutionary Approach And An Empirical Study, Jingyi Wang, Jun Sun, Qixia Yuan, Jun Pang
Learning Probabilistic Models For Model Checking: An Evolutionary Approach And An Empirical Study, Jingyi Wang, Jun Sun, Qixia Yuan, Jun Pang
Research Collection School Of Computing and Information Systems
Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically “learn” models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on …
Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan
Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Recent video captioning methods have made great progress by deep learning approaches with convolutional neural networks (CNN) and recurrent neural networks (RNN). While there are techniques that use memory networks for sentence decoding, few work has leveraged on the memory component to learn and generalize the temporal structure in video. In this paper, we propose a new method, namely Generalized Video Memory (GVM), utilizing a memory model for enhancing video description generation. Based on a class of self-organizing neural networks, GVM’s model is able to learn new video features incrementally. The learned generalized memory is further exploited to decode the …
Vt-Revolution: Interactive Programming Tutorials Made Possible, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Vt-Revolution: Interactive Programming Tutorials Made Possible, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Programming video tutorials showcase programming tasks and associated workflows. Although video tutorials are easy to create, it isoften difficult to explore the captured workflows and interact withthe programs in the videos. In this work, we propose a tool named VTRevolution – an interactive programming video tutorial authoring system. VTRevolution has two components: 1) a tutorial authoring system leverages operating system level instrumentation to log workflow history while tutorial authors are creating programming video tutorials; 2) a tutorial watching system enhances the learning experience of video tutorials by providing operation history and timeline-based browsing interactions. Our tutorial authoring system does not …
Infar: Insight Extraction From App Reviews, Cuiyun Gao, Jichuan Zeng, David Lo, Chin-Yew Lin, Michael R. Lyu, Irwin King
Infar: Insight Extraction From App Reviews, Cuiyun Gao, Jichuan Zeng, David Lo, Chin-Yew Lin, Michael R. Lyu, Irwin King
Research Collection School Of Computing and Information Systems
App reviews play an essential role for users to convey their feedback about using the app. The critical information contained in app reviews can assist app developers for maintaining and updating mobile apps. However, the noisy nature and large-quantity of daily generated app reviews make it difficult to understand essential information carried in app reviews. Several prior studies have proposed methods that can automatically classify or cluster user reviews into a few app topics (e.g., security). These methods usually act on a static collection of user reviews. However, due to the dynamic nature of user feedback (i.e., reviews keep coming …
Identifying Elderly With Poor Sleep Quality Using Unobtrusive In-Home Sensors For Early Intervention, Xiaoping Ma, W K P Neranjana Nadee Rodrigo Goonawardene, Hwee-Pink Tan
Identifying Elderly With Poor Sleep Quality Using Unobtrusive In-Home Sensors For Early Intervention, Xiaoping Ma, W K P Neranjana Nadee Rodrigo Goonawardene, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Along with the upward trend in population ageing is the increasing proportion of the elderly population living alone in the community. This group is especially vulnerable as the onset of various physical, social and mental health issues may be more likely and may go undetected. However, smart homes enabled with elderly monitoring and care systems (EMCS) can now be used to alert caregivers of anomalies in the daily living patterns of the elderly. In this study, we focus on the sleep quality as the key living pattern, as it has been shown that poor sleep quality can lead to health …
Artefact: An R Implementation Of The Autospearman Function, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Artefact: An R Implementation Of The Autospearman Function, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Research Collection School Of Computing and Information Systems
This artefact is the implementation of AutoSpearman, an automated metric selection approach based on correlation analyses. The goal of AutoSpearman is to automatically mitigate correlated metrics prior to constructing analytical models. This artefact is implemented as an R package and is available in the GitHub repository. We provide descriptions and R code snippets for the installation of AutoSpearman and usage examples.
Autospearman: Automatically Mitigating Correlated Software Metrics For Interpreting Defect Models, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Autospearman: Automatically Mitigating Correlated Software Metrics For Interpreting Defect Models, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Research Collection School Of Computing and Information Systems
The interpretation of defect models heavily relies on software metrics that are used to construct them. However, such software metrics are often correlated in defect models. Prior work often uses feature selection techniques to remove correlated metrics in order to improve the performance of defect models. Yet, the interpretation of defect models may be misleading if feature selection techniques produce subsets of inconsistent and correlated metrics. In this paper, we investigate the consistency and correlation of the subsets of metrics that are produced by nine commonly-used feature selection techniques. Through a case study of 13 publicly-available defect datasets, we find …
An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings, Di Wang, Xiaolin Qian, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Xiaofeng Zhang, Geok See Ng, You Zhou
An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings, Di Wang, Xiaolin Qian, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Xiaofeng Zhang, Geok See Ng, You Zhou
Research Collection School Of Computing and Information Systems
Due to their aptitude in both accurate data processing and human comprehensible reasoning, neural fuzzy inference systems have been widely adopted in various application domains as decision support systems. Especially in real-world scenarios such as decision making in financial transactions, the human experts may be more interested in knowing the comprehensive reasons of certain advices provided by a decision support system in addition to how confident the system is on such advices. In this paper, we apply an integrated autonomous computational model termed genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS) to predict underpricing in initial public …
Ten Years Of Hunting For Similar Code For Fun And Profit (Keynote), Stephane Glondu, Lingxiao Jiang, Zhendong Su
Ten Years Of Hunting For Similar Code For Fun And Profit (Keynote), Stephane Glondu, Lingxiao Jiang, Zhendong Su
Research Collection School Of Computing and Information Systems
In 2007, the Deckard paper was published at ICSE. Since its publication, it has led to much follow-up research and applications. The paper made two core contributions: a novel vector embedding of structured code for fast similarity detection, and an application of the embedding for clone detection, resulting in the Deckard tool. The vector embedding is simple and easy to adapt. Similar code detection is also fundamental for a range of classical and emerging problems in software engineering, security, and computer science education (e.g., code reuse, refactoring, porting, translation, synthesis, program repair, malware detection, and feedback generation). Both have buttressed …
Dsm: A Specification Mining Tool Using Recurrent Neural Network Based Language Model, Tien-Duy B. Le, Lingfeng Bao, David Lo
Dsm: A Specification Mining Tool Using Recurrent Neural Network Based Language Model, Tien-Duy B. Le, Lingfeng Bao, David Lo
Research Collection School Of Computing and Information Systems
Formal specifications are important but often unavailable. Furthermore, writing these specifications is time-consuming and requires skills from developers. In this work, we present Deep Specification Miner (DSM), an automated tool that applies deep learning to mine finite-state automaton (FSA) based specifications. DSM accepts as input a set of execution traces to train a Recurrent Neural Network Language Model (RNNLM). From the input traces, DSM creates a Prefix Tree Acceptor (PTA) and leverages the inferred RNNLM to extract many features. These features are then forwarded to clustering algorithms for merging similar automata states in the PTA for assembling a number of …
Using Finite-State Models For Log Differencing, Hen Amar, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Using Finite-State Models For Log Differencing, Hen Amar, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Much work has been published on extracting various kinds of models from logs that document the execution of running systems. In many cases, however, for example in the context of evolution, testing, or malware analysis, engineers are interested not only in a single log but in a set of several logs, each of which originated from a different set of runs of the system at hand. Then, the difference between the logs is the main target of interest. In this work we investigate the use of finite-state models for log differencing. Rather than comparing the logs directly, we generate concise …
On The Sequential Massart Algorithm For Statistical Model Checking, Cyrille Jegourel, Jun Sun, Jin Song Dong
On The Sequential Massart Algorithm For Statistical Model Checking, Cyrille Jegourel, Jun Sun, Jin Song Dong
Research Collection School Of Computing and Information Systems
Several schemes have been provided in Statistical Model Checking (SMC) for the estimation of property occurrence based on predefined confidence and absolute or relative error. Simulations might be however costly if many samples are required and the usual algorithms implemented in statistical model checkers tend to be conservative. Bayesian and rare event techniques can be used to reduce the sample size but they can not be applied without prerequisite or knowledge about the system under scrutiny. Recently, sequential algorithms based on Monte Carlo estimations and Massart bounds have been proposed to reduce the sample size while providing guarantees on error …
A Survey Of Software Metric Use In Research Software Development, Nasir U. Eisty, George K. Thiruvathukal, Jeffrey C. Carver
A Survey Of Software Metric Use In Research Software Development, Nasir U. Eisty, George K. Thiruvathukal, Jeffrey C. Carver
Computer Science: Faculty Publications and Other Works
Background: Breakthroughs in research increasingly depend on complex software libraries, tools, and applications aimed at supporting specific science, engineering, business, or humanities disciplines. The complexity and criticality of this software motivate the need for ensuring quality and reliability. Software metrics are a key tool for assessing, measuring, and understanding software quality and reliability. Aims: The goal of this work is to better understand how research software developers use traditional software engineering concepts, like metrics, to support and evaluate both the software and the software development process. One key aspect of this goal is to identify how the set of metrics …
Liboblivious: A C++ Library For Oblivious Data Structures And Algorithms, Scott D. Constable, Steve Chapin
Liboblivious: A C++ Library For Oblivious Data Structures And Algorithms, Scott D. Constable, Steve Chapin
Electrical Engineering and Computer Science - Technical Reports
Infrastructure as a service (IaaS) is an enormously beneficial model for centralized data computation and storage. Yet, existing network-layer and hardware-layer security protections do not address a broad category of vulnerabilities known as side-channel attacks. Over the past several years, numerous techniques have been proposed at all layers of the software/hardware stack to prevent the inadvertent leakage of sensitive data. This report discusses a new technique which integrates seamlessly with C++ programs. We introduce a library, libOblivious, which provides thin wrappers around existing C++ standard template library classes, endowing them with the property of memory-trace obliviousness.
Issues In Reproducible Simulation Research, Ben G. Fitzpatrick
Issues In Reproducible Simulation Research, Ben G. Fitzpatrick
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
March Of The Silent Bots, Paul Robert Griffin
March Of The Silent Bots, Paul Robert Griffin
MITB Thought Leadership Series
Self-intelligent software robots, or ‘bots’ are everywhere. These small pieces of code run automated tasks when you order a taxi, search for a restaurant or check the weather. Quietly beavering away, it is unknown how many bots exist, but undoubtedly this number is set to surge over time. Already, bots comprise roughly half of all internet traffic.
Disruptive Technology: Can The Banking Industry Harness Disruption For Competitive Edge?, Edgar Low
Disruptive Technology: Can The Banking Industry Harness Disruption For Competitive Edge?, Edgar Low
MITB Thought Leadership Series
Disruptive innovation was identified as a phenomenon more than two decades ago by prominent Harvard scholar Clayton Christensen. So you may wonder why established industries are only now waking up to the prospect of digital transformation - the banking industry in particular.
Computer Games Are Serious Business And So Is Their Quality: Particularities Of Software Testing In Game Development From The Perspective Of Practitioners, Ronnie Santos, Cleyton Magalhaes, Luiz Fernando Capretz, Jorge Correia-Neto, Fabio Q. B. Silva Dr., Abdelrahman Saher
Computer Games Are Serious Business And So Is Their Quality: Particularities Of Software Testing In Game Development From The Perspective Of Practitioners, Ronnie Santos, Cleyton Magalhaes, Luiz Fernando Capretz, Jorge Correia-Neto, Fabio Q. B. Silva Dr., Abdelrahman Saher
Electrical and Computer Engineering Publications
Over the last several decades, computer games started to have a significant impact on society. However, although a computer game is a type of software, the process to conceptualize, produce and deliver a game could involve unusual features. In software testing, for instance, studies demonstrated the hesitance of professionals to use automated testing techniques with games, due to the constant changes in requirements and design, and pointed out the need for creating testing tools that take into account the flexibility required for the game development process. Goal. This study aims to improve the current body of knowledge regarding software …
Scaling-Up Stackelberg Security Games Applications Using Approximations, Arunesh Sinha, Aaron Schlenker, Donnabell Dmello, Milind Tambe
Scaling-Up Stackelberg Security Games Applications Using Approximations, Arunesh Sinha, Aaron Schlenker, Donnabell Dmello, Milind Tambe
Research Collection School Of Computing and Information Systems
Stackelberg Security Games (SSGs) have been adopted widely for modeling adversarial interactions, wherein scalability of equilibrium computation is an important research problem. While prior research has made progress with regards to scalability, many real world problems cannot be solved satisfactorily yet as per current requirements; these include the deployed federal air marshals (FAMS) application and the threat screening (TSG) problem at airports. We initiate a principled study of approximations in zero-sum SSGs. Our contribution includes the following: (1) a unified model of SSGs called adversarial randomized allocation (ARA) games, (2) hardness of approximation for zero-sum ARA, as well as for …
Measuring Program Comprehension: A Large-Scale Field Study With Professionals, Xin Xia, Lingfeng Bao, David Lo, Zhengchang Xing, Ahmed E. Hassan, Shanping Li
Measuring Program Comprehension: A Large-Scale Field Study With Professionals, Xin Xia, Lingfeng Bao, David Lo, Zhengchang Xing, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
During software development and maintenance, developers spend a considerable amount of time on program comprehension activities. Previous studies show that program comprehension takes up as much as half of a developer's time. However, most of these studies are performed in a controlled setting, or with a small number of participants, and investigate the program comprehension activities only within the IDEs. However, developers' program comprehension activities go well beyond their IDE interactions. In this paper, we extend our ActivitySpace framework to collect and analyze Human-Computer Interaction (HCI) data across many applications (not just the IDEs). We follow Minelli et al.'s approach …
Overfitting In Semantics-Based Automated Program Repair, Dinh Xuan Bach Le, Ferdian Thung, David Lo, Claire Le Goues
Overfitting In Semantics-Based Automated Program Repair, Dinh Xuan Bach Le, Ferdian Thung, David Lo, Claire Le Goues
Research Collection School Of Computing and Information Systems
The primary goal of Automated Program Repair (APR) is to automatically fix buggy software, to reduce the manual bug-fix burden that presently rests on human developers. Existing APR techniques can be generally divided into two families: semantics- vs. heuristics-based. Semantics-based APR uses symbolic execution and test suites to extract semantic constraints, and uses program synthesis to synthesize repairs that satisfy the extracted constraints. Heuristic-based APR generates large populations of repair candidates via source manipulation, and searches for the best among them. Both families largely rely on a primary assumption that a program is correctly patched if the generated patch leads …
Implementation Of Secure Dnp3 Architecture Of Scada System For Smart Grids, Uday Bhaskar Boyanapalli
Implementation Of Secure Dnp3 Architecture Of Scada System For Smart Grids, Uday Bhaskar Boyanapalli
Master of Science in Computer Science Theses
With the recent advances in the power grid system connecting to the internet, data sharing, and networking enables space for hackers to maliciously attack them based on their vulnerabilities. Vital stations in the smart grid are the generation, transmission, distribution, and customer substations are connected and controlled remotely by the network. Every substation is controlled by a Supervisory Control and Data Acquisition (SCADA) system which communicates on DNP3 protocol on Internet/IP which has many security vulnerabilities. This research will focus on Distributed Network Protocol (DNP3) communication which is used in the smart grid to communicate between the controller devices. We …
Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon
Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon
Research Collection School Of Computing and Information Systems
Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code …
Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan
Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan
Research Collection School Of Computing and Information Systems
This paper shares our insights on exploring theexperiential learning model and risk management process todesign an undergraduate software architecture course. The keychallenge for undergraduate students to appreciate softwarearchitecture design is usually their limited experience in thesoftware industry. In software architecture, the high-level designprinciples are heuristics lacking the absoluteness of firstprinciples which for inexperienced undergraduate students, thisis a frustrating divergence from what they used to value. From aneducator's perspective, teaching software architecture requirescontending with the problem of how to express this level ofabstraction practically and also make the learning realistic. Inthis paper, we propose a model adapting the concepts ofexperiential learning …
I4s: Capturing Shopper’S In-Store Interactions, Sougata Sen, Archan Misra, Vigneshwaran Subbaraju, Karan Grover, Meeralakshmi Radhakrishnan, Rajesh K. Balan, Youngki Lee
I4s: Capturing Shopper’S In-Store Interactions, Sougata Sen, Archan Misra, Vigneshwaran Subbaraju, Karan Grover, Meeralakshmi Radhakrishnan, Rajesh K. Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
In this paper, we present I4S, a system that identifies item interactions of customers in a retail store through sensor data fusion from smartwatches, smartphones and distributed BLE beacons. To identify these interactions, I4S builds a gesture-triggered pipeline that (a) detects the occurrence of “item picks”, and (b) performs fine-grained localization of such pickup gestures. By analyzing data collected from 31 shoppers visiting a mid-sized stationary store, we show that we can identify person-independent picking gestures with a precision of over 88%, and identify the rack from where the pick occurred with 91%+ precision (for popular racks).
Automating Intention Mining, Qiao Huang, Xin Xia, David Lo, Gail C. Murphy
Automating Intention Mining, Qiao Huang, Xin Xia, David Lo, Gail C. Murphy
Research Collection School Of Computing and Information Systems
Developers frequently discuss aspects of the systems they are developing online. The comments they post to discussions form a rich information source about the system. Intention mining, a process introduced by Di Sorbo et al., classifies sentences in developer discussions to enable further analysis. As one example of use, intention mining has been used to help build various recommenders for software developers. The technique introduced by Di Sorbo et al. to categorize sentences is based on linguistic patterns derived from two projects. The limited number of data sources used in this earlier work introduces questions about the comprehensiveness of intention …
Revisiting Supervised And Unsupervised Models For Effort-Aware Just-In-Time Defect Prediction, Qiao Huang, Xin Xia, David Lo
Revisiting Supervised And Unsupervised Models For Effort-Aware Just-In-Time Defect Prediction, Qiao Huang, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
Effort-aware just-in-time (JIT) defect prediction aims at finding more defective software changes with limited code inspection cost. Traditionally, supervised models have been used; however, they require sufficient labelled training data, which is difficult to obtain, especially for new projects. Recently, Yang et al. proposed an unsupervised model (i.e., LT) and applied it to projects with rich historical bug data. Interestingly, they reported that, under the same inspection cost (i.e., 20 percent of the total lines of code modified by all changes), it could find about 12% - 27% more defective changes than a state-of-the-art supervised model (i.e., EALR) when using …
Categorizing The Content Of Github Readme Files, Gede Artha Azriadi Prana, Christoph Treude, Ferdian Thung, Thushari Atapattu, David Lo
Categorizing The Content Of Github Readme Files, Gede Artha Azriadi Prana, Christoph Treude, Ferdian Thung, Thushari Atapattu, David Lo
Research Collection School Of Computing and Information Systems
README files play an essential role in shaping a developer’s first impression of a software repository and in documenting the software project that the repository hosts. Yet, we lack a systematic understanding of the content of a typical README file as well as tools that can process these files automatically. To close this gap, we conduct a qualitative study involving the manual annotation of 4,226 README file sections from 393 randomly sampled GitHub repositories and we design and evaluate a classifier and a set of features that can categorize these sections automatically. We find that information discussing the ‘What’ and …
Function Risk Assessment Under Memory Leakage, Jianming Fu, Rui Jin, Yan Lin, Baihe Jiang, Zhengwei Guo
Function Risk Assessment Under Memory Leakage, Jianming Fu, Rui Jin, Yan Lin, Baihe Jiang, Zhengwei Guo
Research Collection School Of Computing and Information Systems
Code reuse attack (CRA), specifically reusing and then reconstructing the codes (gadgets) already existed in programs and libraries, is widely exploited in software attacks. Admittedly, determination of the location of the gadgets consisted of target instructions along with control flow transfer instructions, is of critical importance. Address Space Randomization (ASR), which serves as an effective technique to mitigate CRA, increases the entropy by randomizing the location of the code or data, and baffles adversaries from figuring out the memory layout. Currently, variable randomization methods of high granularity are proposed by scholars to prevent adversaries from deducting memory layout. However, their …
Mixed-Reality For Object-Focused Remote Collaboration, Martin Feick, Anthony Tang, Scott Bateman
Mixed-Reality For Object-Focused Remote Collaboration, Martin Feick, Anthony Tang, Scott Bateman
Research Collection School Of Computing and Information Systems
In this paper we outline the design of a mixed-reality system to support object-focused remote collaboration. Here, being able to adjust collaborators' perspectives on the object as well as understand one another's perspective is essential to support effective collaboration over distance. We propose a low-cost mixed-reality system that allows users to: (1) quickly align and understand each other's perspective; (2) explore objects independently from one another, and (3) render gestures in the remote's workspace. In this work, we focus on the expert's role and we introduce an interaction technique allowing users to quickly manipulation 3D virtual objects in space.